In order to optimize the current-control performance of the permanent-magnet synchronous motor (PMSM) system with model parameter mismatch and one-step control delay, an improved deadbeat predictive current control (DPCC) algorithm for the PMSM drive systems is proposed in this paper. First, the performance of the conventional predictive current control, when parameter mismatch exist, is analyzed, and then a stator current and disturbance observer (SCDO) based on sliding-mode exponential reaching law, which is able to simultaneously predict future value of stator current and track system disturbance caused by parameter mismatch in real time, is proposed. Based on this SCDO, prediction currents are used for replacing the sampled current in DPCC to compensate one-step delay, and estimated parameter disturbances are considered as the feedforward value to compensate the voltage reference calculated by deadbeat predictive current controller. Thus, a composite control method combining the DPCC part and current prediction and feedforward compensation part based on SCDO, called DPCC + SCDO method, is developed. Moreover, based on conventional exponential reaching law, a novel sliding-mode exponential reaching law is proposed to further improve the performance of the DPCC + SCDO method. Simulation and experimental results both show the validity of the proposed current control approach.

Deadbeat Predictive Current Control of Permanent-Magnet Synchronous Motors with Stator Current and Disturbance Observer

In order to solve the parameter dependence problem in model predictive control, an improved model predictive current control (MPCC) method based on the incremental model for surface-mounted permanent-magnet synchronous motor drives is proposed in this paper. First, the parameter sensitivity of a conventional MPCC method is analyzed, which indicates that the parameter mismatches would cause prediction current error and inaccurate delay compensation. Therefore, an incremental prediction model is introduced in this paper to eliminate the use of permanent magnetic flux linkage in a prediction model. Among the parameter of the incremental prediction model, only inductance mismatch contributes to the prediction error, since the influence of resistance mismatch on the control performance is very small. Therefore, in order to improve the antiparameter-disturbance capability of the MPCC method, an inductance disturbance controller, which includes the inductance disturbance observer and inductance extraction algorithm, is presented to update accurate inductance information for the whole control system in real time. Finally, simulation and experimental results both show that the proposed method can effectively eliminate the influence of the parameter mismatches on the control performance and reduce the parameter sensitivity of the MPCC method.

Model Predictive Current Control for PMSM Drives With Parameter Robustness Improvement

A robust predictive current control algorithm for permanent magnet synchronous motor (PMSM) drives is proposed. The robust controller adopts an incremental system model, thus can operate without knowing the rotor flux, which is a pivotal parameter in conventional dead-beat control. The incremental model is easy to implement compared with relevant flux identification algorithms or observers. The robust controller adopts an extended state observer (ESO) to enhance inductance robustness to cater for the divergence caused by stator inductance mismatch. The inductance mismatch uncertainties can be estimated and compensated by ESO. Conventional dead-beat control is modified for multistep prediction to achieve high disturbance rejection. Detailed stability and disturbance rejection analysis are conducted theoretically, and rules on parameter selection are given. Comparative simulations and experimentations verify the effectiveness and superiority of proposed robust control over conventional dead-beat control.

Flux Immunity Robust Predictive Current Control With Incremental Model and Extended State Observer for PMSM Drive

To promote the drive performance of permanent magnet synchronous machine (PMSM), such as tracking accuracy of both speed and current, one improved deadbeat-based predictive current control (DPCC) scheme based sliding mode is proposed in this paper. First, one novel PMSM model is derived by considering uncertainties of both parameters and external disturbances. Second, in order to improve the dynamic response of PMSM drive system, both sliding mode control (SMC) and DPCC are employed to control the speed and current, respectively. Third, a unified high-order sliding mode observer is designed for the estimation of disturbances and uncertainties in the speed and current loops. Furthermore, the estimated values are compensated with feedback to the designed SMC and DPCC to increase the speed robustness and current tracking accuracy. Comprehensive simulation and experiments demonstrate that the proposed control strategy is strongly robust to acute variations of load and machine parameters, and it is testified to have better speed and current tracking performance.

Improved Deadbeat Predictive Current Control Combined Sliding Mode Strategy for PMSM Drive System

In this paper, the behavior of a grid-connected hybrid ac/dc microgrid has been investigated. Different renewable energy sources—photovoltaics modules and a wind turbine generator—have been considered together with a solid oxide fuel cell and a battery energy storage system. The main contribution of this paper is the design and the validation of an innovative online-trained artificial neural network-based control system for a hybrid microgrid. Adaptive neural networks are used to track the maximum power point of renewable energy generators and to control the power exchanged between the front-end converter and the electrical grid. Moreover, a fuzzy logic-based power management system is proposed in order to minimize the energy purchased from the electrical grid. The operation of the hybrid microgrid has been tested in the MATLAB/Simulink environment under different operating conditions. The obtained results demonstrate the effectiveness, the high robustness and the self-adaptation ability of the proposed control system.

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Adaptive Neural Network-Based Control of a Hybrid AC/DC Microgrid

This paper presents a novel on-line inertia identification method with a load torque observer to optimize the speed loop PID parameters of a servo system. The proposed inertia identification algorithm in this paper adopts the fixed-order recursive empirical frequency-domain optimal parameter estimation to improve the speed loop performance. A load torque observer is employed in order to obtain a more precise value of inertia. Then, the method of speed loop PI parameters optimization with the identified inertia and load torque is deduced in frequency domain. Compared with the recursive least square algorithm, the effectiveness of the proposed method is demonstrated by the simulation and experimental results.

On-line Inertia Identification Algorithm for PI Parameters Optimization in Speed Loop

The invention relates to a method for determining an initial position angle of a rotor of a permanent magnet synchronous motor, which belongs to the field of motor control. The invention aims to solve the problems that a rotor detection method provided by a patent of a method and a device for detecting an initial position of a motor rotor in an alternating current servo system has complex logical decision and easy failure of detection. The method for determining the initial position angle of the rotor of the permanent magnet synchronous motor comprises the following steps: controlling the amplitude and the phase of the current of a d shaft and a q shaft under a two-phase synchronous rotating shaft system to further control the amplitude and the phase of the current of a three-phase stator under a three-phase stationary shaft system; controlling the rotation of the motor rotor under a given stator magnetic field; and according to a pulse signal fed back by an incremental encoder, making the direction of the given magnetic field gradually and circularly approach the actual position angle of the rotor until the direction of the given stator magnetic field corresponding to the current of the d shaft and the q shaft is coincident with the direction of a rotor magnetic field. The direction of a given stator current magnetic field is changed by 90 degrees, so that a torque vertical to the direction of the rotor magnetic field is generated on the rotor, and according to the rotating direction of the rotor, the initial position angle of the actual rotor is determined.

Method for determining initial position angle of rotor of permanent magnet synchronous motor

The predictive current control algorithm of permanent magnet synchronous motor (PMSM) drives has precise current tracking, constant switching frequency and is suitable for digital implementation. However, the conventional deadbeat predictive current control algorithm is sensitive to the stator inductance parameter mismatch. This paper presents an improved deadbeat predictive current control algorithm while achieving significantly increased robustness to inductance parameter mismatch. The current offset constraint and output voltage prediction method were modified. The relationship between system stability and inductance mismatch was analyzed using root locus method. In addition, the proposed algorithm is very simple and can be effectively implemented. Simulation results demonstrate the effectiveness of the proposed predictive current control algorithm. The system robustness is proven.

Improved deadbeat predictive current control strategy for permanent magnet motor drives

In this paper a predictive current control for PMSM based on dead-beat algorithm is proposed, in the synchronous rotary frame, to improve the performance of current loop Based on the mathematical model of PMSM the current controller calculated the expected voltage vector directly in terms of the current reference and feedback values, after that transforms the voltage vector into switch signals with SVPWM model The robust predictive current control is introduced to decrease the sensitivity of system stability with the model parameters error Simulation and experiment results show that the PMSM predictive current control scheme improves both the dynamic performance and steady-state precision of the current loop efficiently

Deadbeat predictive current control for PMSM

The dynamic response of the permanent magnet AC servo system is restricted by the bandwidth of current loop, which is the innermost loop. In digital control AC servo system, there are two major limiting factors for current-loop bandwidth, switching frequency and digital delay which means sum of A/D sampling time, algorithm execution time and PWM duty cycle update delay. The bandwidth expansion of current-loop without changing the switching frequency of power devices is necessary, as the increasing of switching frequency will raise the losses. Based on the synchronous rotating frame current decoupled control, the delay effects of current sampling and PWM duty cycle update in the permanent magnet synchronous motor drive systems were analyzed. The bandwidth expansion strategy was proposed, to achieve the stator current double sampling and PWM duty cycle double update in a carrier period. The current-loop bandwidth can be extended more than one times theoretically while the switching frequency remaining unchanged, so that the dynamic performance of permanent magnet synchronous motor drive systems was improved. Simulation and experimental results verified the theoretical analysis and effectiveness of the method.

Li Niu

Papers

On-line Inertia Identification Algorithm for PI Parameters Optimization in Speed Loop

Method for determining initial position angle of rotor of permanent magnet synchronous motor

Improved deadbeat predictive current control strategy for permanent magnet motor drives

Deadbeat predictive current control for PMSM

Current-loop bandwidth expansion strategy for permanent magnet synchronous motor drives